PROFESSIONAL SUMMARY:

Responsible for designing and implementing the infrastructure needed to store, process and analyze huge data sets by collaborating with cross - functional teams and customer.

TECHNICAL SKILLS:

Big Data Platform: Hadoop, Confidential, Cloudera, Hortonworks

Cloud Implementation: AWS, Azure, Oracle(OCI), Cloud Formation, Terraform

Micro-services: Docker, Kubernetes, Mesosphere

Data Ingestion/ETL: Spark, Kafka, Pig, Hive, Elastic Search, Flume, Talend

NoSQL/ MPP DB: Cassandra, HBase, MongoDB, Presto, EnterpriseDB

Scripting Tools: R-Studio, Python, Scala, Perl, Zeppelin

BI Tools: Tableau, Kibana, Pentaho

PROFESSIONAL EXPERIENCE:

Solutions Architect

Confidential, Dallas

Responsibilities:

• Performing hands-on architecture and solution design of analytics applications used by customers and their internal users at scale on Cloud Infrastructure.
• Building solutions that use Apache Kafka alongside Hadoop, relational and NoSQL databases, message queues, and related products as MPP Database, Scala and Python.
• Working closely with Management and Decision Science team in the identification, acquisition, cleansing and visualization of data to support various data analytical projects.
• Preparing and presenting potential technical solutions, advice business, and product owners on the technical value of proposals, including tradeoffs and opportunities.
• Building next generation data services platform using Microservice, Docker Container, Kubernetes and Cloud native services.

Data Architect

Confidential, Dallas

Responsibilities:

• Developed data pipeline for advanced analytics, with processing frameworks like Hadoop, Apache Spark and Streaming technologies such as Kafka, Flume and Kinesis.
• Build highly scalable and extensible Big Data platforms on AWS, which enabled collection and analysis of massive data sets including those from IoT and streaming data.
• Managed Hadoop and Spark cluster environments, on bare-metal and container infrastructure, including configuration for the cluster and capacity planning.
• Leading the tool selection and development of key projects implementing Big Data technologies with client's On-Premise data centers or using Cloud Infrastructure.

Hadoop Lead Engineer

Confidential, Chicago

Responsibilities:

• Developed automation framework for installation of Hadoop ecosystem components like HBase, HDFS, Map/Reduce, Yarn, Oozie, Pig, Hive, Impala, Spark and Kafka.
• Leveraging the ETL tools like Pentaho, Control-M and Subversion for developing efficient solutions for data management, conversion, migration and integration.
• Migrated legacy POS application data from Mainframe to Hadoop distributed platform.
• Applying statistical techniques such as Segmentation Analysis, Time Series Analysis to develop and monitor scorecards.
• Worked with Data Science team to implement the machine learning algorithms for advanced analytics use cases including demand forecasting, classification and clustering.

Senior Data Engineer

Confidential

Responsibilities:

• Implemented Big Data ecosystem (Hadoop, Map Reduce, Sqoop HBase, Hive, Pig and Mongo DB) to derive insights/analytics from data.
• Build applications for finding baseline stats with mean, deviation and implementing Social Media Analysis, Recommendation and Trends Prediction using R programming.
• Collaborating with project teams (managers, architects, data science) on tools and technology related to the design and development of Big Data solutions in Agile.

Data Engineer

Confidential

Responsibilities:

• Developed Entity-Relationship models for Retail, Insurance, and Life Science customer. Specific work in modeling data warehouse and operational data store.
• Experience with advanced level of SQL programming and performance tuning techniques for Data Integration and Consumption within an OLTP, OLAP, and MPP architecture.
• Build ETL mappings for processing fact, dimension tables with complex transformation.
• Developed key modules Mappings, Workflows using Informatica, Reports using OBIEE.